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The aim is to propose a novel framework for understanding machine cognition by redefining models as ontological spaces.

March 18, 2026Open Access

Model World: A Prolegomenon to an Environmental Ontology of Machine Cognition

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The aim is to propose a novel framework for understanding machine cognition by redefining models as ontological spaces.
Analyzed the limitations of the prevalent model-as-brain metaphor in AI.
Proposed a new perspective where models function as worlds encoding human cognitive structures.
Examined the implications of this framework on AI capabilities, focusing on the roles of inference and teleology.
Proposed that the constraints on machine cognition are primarily influenced by the depth of will, rather than model size or architecture.
Identified logic and mathematics as emergent properties rather than programmed features in AI systems.
Highlighted that a dense information environment is critical for machine cognition to function effectively.

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Argues for a new framework of machine cognition, suggesting implications for AI capabilities and limits.

Guy Egozy (2026) studied this question.

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